Integration of Ecosystem Services into the Assessment of Forest Landscape Restoration in Tropical Africa: An Exploratory Review
Bibliographic record
Abstract
Forest landscape restoration (FLR) in tropical Africa seeks to improve the ability of degraded forest to provide ecosystem services (ESs) to local communities. The purpose of this study is to present ESs that are mentioned in studies on FLR and methods that best integrate the different categories of ESs that have been identified in tropical Africa. The study followed the PRISMA 2020 statement for reporting systematic reviews. Qualitative and quantitative data were analyzed using agglomerative clustering and multiple correspondence analysis (MCA). The systematic literature review analyzes modalities of ES integration through various studies on FLR in tropical Africa. In most cases, only three of the four ES categories are mentioned, namely provisioning, regulating and supporting services. Primary production is the ES category most frequently mentioned in tropical Africa. In this region, various methods are used to restore forest landscapes (reforestation, savannah protection, agroforestry). This review shows a strong link between ESs, the ES categories, use values and methods of FLR. Therefore, integration of ESs in FLR can contribute to the understanding of how FLR impacts biodiversity, climate change mitigation. improvement of human well-being, etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".